An integrated Artificial Intelligence and GIS spatial analyst tools for Delineation of Groundwater Potential Zones in complex terrain: Fincha Catchment, Abay Basi, Ethiopia

IF 3.5 Q2 ENVIRONMENTAL SCIENCES

Air Soil and Water Research Pub Date : 2022-01-01 DOI:10.1177/11786221211045972

Habtamu Tamiru, Meseret Wagari, Bona Tadese

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引用次数: 2

Abstract

In this paper, the performance of Artificial Intelligence (AI) in Geospatial analysis and GIS platforms for the prospecting of potential groundwater zones was evaluated in Fincha catchment, Abay, Ethiopia. Components of geospatial data under morphometric, hydrologic, permeability, and surface dynamic change were confirmed as the criteria for prospecting groundwater potential zones. The influence of the individual criterion was ranked and weighted in Artificial Neural Networks (ANN) training model and Analytical Hierarchy Process (AHP). The correctness of the weights fixed in the ANN and AHP was evaluated with target data assigned to the networks and consistency index (CI) respectively. The weighted overlay analysis in the GIS environment was implemented to generate the promising zones in both approaches (ANN and GIS). The results obtained in the ANN model and GIS were evaluated based on pumping rate and ground-truthing points. Groundwater potential zones of five and four classes were delineated in AI and GIS techniques respectively, and this is an indicator for the effectiveness of AI in geospatial analysis for prospecting of potential zones than the traditional GIS technique. The percentage of accuracy in both methods was measured from the ROC curve and AUC. Therefore, it was found that the delineated groundwater potential zones and the ground-truthing points were agreed with 96% and 91% in the AI and GIS platforms respectively. Finally, it is concluded that the ANN model is an effective tool for the delineation of groundwater prospective zones.

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用于在复杂地形中划定地下水潜力区的集成人工智能和GIS空间分析工具：埃塞俄比亚阿巴伊盆地芬查流域

在本文中，评估了人工智能（AI）在埃塞俄比亚阿拜芬查流域勘探潜在地下水带的地理空间分析和GIS平台中的性能。地貌、水文、渗透性和地表动态变化下的地理空间数据成分被确认为勘探地下水潜力区的标准。在人工神经网络（ANN）训练模型和层次分析法（AHP）中对个体准则的影响进行了排序和加权。分别用分配给网络的目标数据和一致性指数（CI）来评估ANN和AHP中固定权重的正确性。在GIS环境中实施加权叠加分析，以在两种方法（ANN和GIS）中生成有希望的区域。基于抽水率和地面实况点对ANN模型和GIS中获得的结果进行了评估。利用人工智能和地理信息系统技术分别划定了五类和四类地下水潜力区，这是人工智能在勘探潜力区的地理空间分析中比传统地理信息系统方法有效性的指标。根据ROC曲线和AUC测量两种方法的准确率。因此，研究发现，在AI和GIS平台上，划定的地下水潜力区和地面实况点分别与96%和91%一致。最后得出结论，ANN模型是划定地下水远景区的有效工具。

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来源期刊

Air Soil and Water Research ENVIRONMENTAL SCIENCES-

CiteScore

7.80

自引率

5.30%

发文量

审稿时长

8 weeks

期刊介绍： Air, Soil & Water Research is an open access, peer reviewed international journal covering all areas of research into soil, air and water. The journal looks at each aspect individually, as well as how they interact, with each other and different components of the environment. This includes properties (including physical, chemical, biochemical and biological), analysis, microbiology, chemicals and pollution, consequences for plants and crops, soil hydrology, changes and consequences of change, social issues, and more. The journal welcomes readerships from all fields, but hopes to be particularly profitable to analytical and water chemists and geologists as well as chemical, environmental, petrochemical, water treatment, geophysics and geological engineers. The journal has a multi-disciplinary approach and includes research, results, theory, models, analysis, applications and reviews. Work in lab or field is applicable. Of particular interest are manuscripts relating to environmental concerns. Other possible topics include, but are not limited to: Properties and analysis covering all areas of research into soil, air and water individually as well as how they interact with each other and different components of the environment Soil hydrology and microbiology Changes and consequences of environmental change, chemicals and pollution.